Fast Bit-Vector Algorithms for Approximate String Matching Under Indel Distance

Hyyrö, Heikki; Pinzón, Yoan J.; Shinohara, Ayumi

doi:10.1007/978-3-540-30577-4_44

Cited by 3 publications

(7 citation statements)

References 3 publications

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“…But IndelNew needs to explicitly deal with the case M when computing the +1 difference vectors P h j and P v j , unless these vectors are computed implicitly/indirectly. The latter approach was employed in IndelMYE algorithm [3] by using vertical and horizontal zero difference vectors Zv j and Zh j , where…”

Section: Rightmost Differences D[i J]−d[i J−1] and D[i J]−d[i−1 J]mentioning

confidence: 99%

“…Fig. 6 shows the complete algorithm IndelMYE as presented in [3]. IndelNew algorithm is able to compute the positive vectors directly.…”

Section: Now Each Set Bitmentioning

confidence: 99%

“…In [3] the three above-mentioned bit-parallel algorithms were extended to support the indel distance. In this paper we improve the running time of one of those algorithms, namely, IndelMYE.…”

Section: Introductionmentioning

confidence: 99%

“…In Sections 3 we explain the main bit-parallel ideas used to create the new algorithm presented in Section 4. In Section 5 we present extensive experimental results for the three bit-parallel variants presented in [3] and two dynamic programming algorithms. Finally, in Section 6 we give our conclusions.…”

Section: Introductionmentioning

confidence: 99%

“…It is common to use Levenshtein distance [5], which allows the differences to be single-character insertions, deletions, substitutions. Recently, in [3], the IndelMYE, IndelWM and IndelBYN algorithms where introduced as modified version of the bit-parallel algorithms of Myers [6], Wu&Manber [10] and BaezaYates&Navarro [1], respectively. These modified versions where made to support the indel distance (only single-character insertions and/or deletions are allowed).…”

mentioning

confidence: 99%

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New Bit-Parallel Indel-Distance Algorithm

Hyyrö

Pinzón

Shinohara

2005

Experimental and Efficient Algorithms

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Abstract. The task of approximate string matching is to find all locations at which a pattern string p of length m matches a substring of a text string t of length n with at most k differences. It is common to use Levenshtein distance [5], which allows the differences to be single-character insertions, deletions, substitutions. Recently, in [3], the IndelMYE, IndelWM and IndelBYN algorithms where introduced as modified version of the bit-parallel algorithms of Myers [6], Wu&Manber [10] and BaezaYates&Navarro [1], respectively. These modified versions where made to support the indel distance (only single-character insertions and/or deletions are allowed). In this paper we present an improved version of IndelMYE that makes a better use of the bit-operations and runs 24.5 percent faster in practice. In the end we present a complete set of experimental results to support our findings.

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Section: Rightmost Differences D[i J]−d[i J−1] and D[i J]−d[i−1 J]mentioning

confidence: 99%

“…Fig. 6 shows the complete algorithm IndelMYE as presented in [3]. IndelNew algorithm is able to compute the positive vectors directly.…”

Section: Now Each Set Bitmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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New Bit-Parallel Indel-Distance Algorithm

Hyyrö

Pinzón

Shinohara

2005

Experimental and Efficient Algorithms

Self Cite

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A hash trie filter method for approximate string matching in genomic databases

2010

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In genomic databases, approximate string matching with k errors is often applied when searching genomic sequences, where k errors can be caused by substitution, insertion, or deletion operations. In this paper, we propose a new method, the hash trie filter, to efficiently support approximate string matching in genomic databases. First, we build a hash trie for indexing the genomic sequence stored in a database in advance. Then, we utilize an efficient technique to find the ordered subpatterns in the sequence, which could reduce the number of candidates by pruning some unreasonable matching positions. Moreover, our method will dynamically decide the number of ordered matching grams, resulting in the increase of precision. The simulation results show that the hash trie filter outperforms the well-known (k + s) q-samples filter in terms of the response time, the number of verified candidates, and the precision, under different lengths of the query patterns and different error levels.

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Shifted Hamming distance: a fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping

et al. 2015

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We present a simple and efficient algorithm, Shifted Hamming Distance (SHD), which accelerates the alignment verification procedure in read mapping, by quickly filtering out error-abundant sequence pairs using bit-parallel and SIMD-parallel operations. SHD only filters string pairs that contain more errors than a user-defined threshold, making it fully comprehensive. It also maintains high accuracy with moderate error threshold (up to 5% of the string length) while achieving a 3-fold speedup over the best previous algorithm (Gene Myers's bit-vector algorithm). SHD is compatible with all mappers that perform sequence alignment for verification.

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Fast Bit-Vector Algorithms for Approximate String Matching Under Indel Distance

Cited by 3 publications

References 3 publications

New Bit-Parallel Indel-Distance Algorithm

New Bit-Parallel Indel-Distance Algorithm

A hash trie filter method for approximate string matching in genomic databases

Shifted Hamming distance: a fast and accurate SIMD-friendly filter to accelerate alignment verification in read mapping

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